The present invention relates to an inspection method and apparatus for comparing an image of an object, which is obtained by using light, laser beams, or the like, and a reference image to detect a fine pattern defect, a foreign body, or the like from a difference between the images. In particular, the present invention relates to a pattern inspection apparatus that is designed preferably for performing visual inspection of a semiconductor wafer, a TFT, a photomask, and the like, and a method therefor.
As an example of a conventional technique for comparing an inspection object image and a reference image to detect a defect, reference is made to a method described in JP-A-05-264467. This method involves sequentially sensing images of an inspection object specimen using a line sensor, in which repetitive patterns are arranged regularly, comparing the sensed images with images delayed by a time for establishing a repetitive pattern pitch, and detecting a non-coincident part of the images as a pattern defect.
Such a conventional inspection method will be described in conjunction with the visual inspection of a semiconductor wafer, as an example. In a semiconductor wafer which serves as an object of inspection, as shown in FIG. 6, a large number of chips having an identical pattern are arranged regularly. As shown in FIG. 7, each chip can be roughly divided into a memory mat section 71 and a peripheral circuit section 72. The memory mat section 71 consists of a set of small repetitive patterns (cells), and the peripheral circuit section 72 basically consists of a set of random patterns. In general, in the memory mat section 71, the pattern density is high, and an image thereof obtained by a bright-field illumination optical system tends to be dark. On the other hand, in the peripheral circuit section 72, the pattern density is low, and an image thereof tends to be bright.
In the conventional visual inspection, images in the same positions of two chips adjacent to each other, for example, an area 61, an area 62, and the like, as seen in FIG. 6, are compared, and a difference between the images is detected as a defect. In this case, since there may be a vibration of the stage which holds the object, inclination of the object, or the like, the positions of the two images do not always coincide with each other. Thus, the amount of positional, deviation of the image sensed by the sensor and the image delayed by the repetitive pattern pitch is determined, the two images are aligned on the basis of the amount of positional deviation, and then a difference between the images is calculated. When the difference is larger than a specified threshold value, it is judged that there is a defect in the pattern; and, when the difference is smaller than the threshold value, it is judged that there is no defect in the pattern.
In the alignment of two images in the comparative inspection, it is a general practice to set the edge parts in the images as one piece of information for calculation of the amount of positional deviation and to calculate the amount of positional deviation such that deviation of the edge parts between the images is minimized. Actually, a method has been proposed using normalized cross correlation, as well as a method using a sum of residuals, and the like. However, in any of such methods, since the amount of calculation is enormous, in order to realize speedup of the inspection, various measures have been required, such as changing the positional deviation calculation section to hardware or increasing the number of arithmetic operation circuits, and change of the image processing algorithm, such as simplification of the calculation of the amount of positional deviation.
In addition, in a semiconductor wafer which serves as the object of inspection, a slight difference in thickness occurs in a pattern due to planarization by CMP or the like, and so there is a difference in the brightness locally in images among chips. For example, reference numeral 41 in FIG. 4A denotes an example of an inspection object image, and reference numeral 42 in FIG. 4B denotes an example of a reference image. As indicated by the pattern 4a in FIG. 4A and the pattern 4b in FIG. 4B, a difference in the brightness occurs in an identical pattern of the inspection object image and the reference image.
In addition, there is a defect 4d in the inspection object image 41 of FIG. 4A. A difference image in this case is as shown in FIG. 4C. The difference image is an image represented by a density difference according to a differential value in corresponding positions of an inspection object image and a reference image. A waveform of a differential value in position 1D-1D′ is as shown in FIG. 4D. With respect to such an image, if a part where the differential value is equal to or more than a specific threshold value TN is regarded as a defect, as in the conventional system, a differential value 4c of the patterns 4a and 4b, which are different in brightness, is detected as a defect. However, this condition should not originally be detected as a defect. In other words, this is a false defect or a nuisance defect (hereinafter referred to as a false defect). Conventionally, as one method of avoiding the occurrence of a false defect, such as indicated by the differential value 4c of FIG. 4C, the threshold value TN is increased (from TH to TH2 in FIG. 4D). However, this leads to a decrease in the sensitivity, and the defect 4d with a differential value of the same or lower level cannot be detected.
In addition, a difference in the brightness due to a difference in the thickness may occur only among specific chips within a wafer such as shown in FIG. 6, or it may occur only in a specific pattern within a chip. However, if the threshold value TH is adjusted to these local areas, the overall inspection sensitivity will be extremely decreased.